基于异构多智能体强化学习的双模通信系统跨媒介资源联合分配方法

陈智雄; 韦梓涵

doi:10.11959/j.issn.1000-0801.DXKX260462

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基于异构多智能体强化学习的双模通信系统跨媒介资源联合分配方法

研究与开发 | 更新时间：2026-09-14

- 基于异构多智能体强化学习的双模通信系统跨媒介资源联合分配方法
- Joint Cross-Medium Resource Allocation Algorithm for Dual-mode Communication Systems Based on Heterogeneous Multi-agent Reinforcement Learning
- 电信科学 2026年
- 作者机构：
  
  1.华北电力大学燕赵电力实验室，河北保定 071003
  2.华北电力大学电子与通信工程系，河北保定 071003
  3.河北省电力物联网技术重点实验室，河北保定 071003
- 作者简介：
  
  陈智雄（1983-），男，博士，华北电力大学电子与通信工程系副教授，主要研究方向为电力物联网、电力线通信。
  韦梓涵（2001-），男，华北电力大学电子与通信工程系硕士生，主要研究方向为电力线通信。
- 基金信息：
  
  河北省省级科技计划资助(26364503D);河北省中央引导地方科技专项项目资助(264Z2101G);国家自然科学基金资助项目;The National Natural Science Foundation of China(61601182)
- DOI：10.11959/j.issn.1000-0801.DXKX260462
  中图分类号： TN91
- 收稿：2026-07-06，
  
  修回：2026-07-28，
  
  录用：2026-08-20，
- 稿件说明：
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陈智雄, 韦梓涵. 基于异构多智能体强化学习的双模通信系统跨媒介资源联合分配方法[J/OL]. 电信科学, 2026.

CHEN Zzhixiong, WEI Zihan. Joint Cross-Medium Resource Allocation Algorithm for Dual-mode Communication Systems Based on Heterogeneous Multi-agent Reinforcement Learning[J/OL]. Telecommunications Science, 2026.
陈智雄, 韦梓涵. 基于异构多智能体强化学习的双模通信系统跨媒介资源联合分配方法[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260462.

CHEN Zzhixiong, WEI Zihan. Joint Cross-Medium Resource Allocation Algorithm for Dual-mode Communication Systems Based on Heterogeneous Multi-agent Reinforcement Learning[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260462.

摘要

针对低压台区计量网络中双模通信系统信道环境时变、业务需求波动性强、存在通信媒介差异及资源协同调度困难的问题，提出一种基于异构多智能体强化学习的双模通信系统跨媒介资源联合分配方法。基于Lyapunov优化理论将长期队列稳定约束转化为逐时隙在线优化问题，设计了按用户通信模式划分的差异化异构智能体架构，提出了自适应跨媒介资源调控机制，在保障长期队列稳定的前提下，实现了二维资源块与功率在线跨媒介联合分配。仿真结果表明，所提算法在用户供需匹配度(User Supply-demand Ratio

USR)、平均时延和公平性上均优于对比算法，其中USR提升3.5%，平均时延降低8.1%，公平性指数提升1.0%。

Abstract

In the low-voltage distribution area metering network with dual-mode communication

challenges arise from time-varying channel conditions

highly fluctuating service demands

heterogeneous communication media

and difficulty in coordinated resource scheduling. To tackle these issues

we propose a cross-media joint resource allocation method based on heterogeneous multi-agent reinforcement learning. Leveraging Lyapunov optimization

we transform the long-term queue stability constraint into a per-timeslot online optimization problem. A differentiated agent architecture is designed

where agents are categorized according to user communication modes

and an adaptive cross-media resource regulation mechanism is introduced. This enables online joint allocation of two-dimensional resource blocks and transmission power across media while ensuring long-term queue stability. Simulation results demonstrate that the proposed algorithm outperforms baseline schemes in terms of User Supply-demand Ratio(USR)

average delay

and fairness index. Specifically

the USR is improved by 3.5%

the average delay is reduced by 8.1%

and the fairness index is increased by 1.0%.

关键词

Keywords

references

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